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dc.contributor.authorSchweinberger, Michael
Handcock, Mark S.
dc.date.accessioned 2017-06-14T18:46:24Z
dc.date.available 2017-06-14T18:46:24Z
dc.date.issued 2015
dc.identifier.citation Schweinberger, Michael and Handcock, Mark S.. "Local dependence in random graph models: characterization, properties and statistical inference." Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77, no. 3 (2015) Wiley: 647-676. https://doi.org/10.1111/rssb.12081.
dc.identifier.urihttps://hdl.handle.net/1911/94849
dc.description.abstract Dependent phenomena, such as relational, spatial and temporal phenomena, tend to be characterized by local dependence in the sense that units which are close in a well-defined sense are dependent. In contrast with spatial and temporal phenomena, though, relational phenomena tend to lack a natural neighbourhood structure in the sense that it is unknown which units are close and thus dependent. Owing to the challenge of characterizing local dependence and constructing random graph models with local dependence, many conventional exponential family random graph models induce strong dependence and are not amenable to statistical inference. We take first steps to characterize local dependence in random graph models, inspired by the notion of finite neighbourhoods in spatial statistics and M-dependence in time series, and we show that local dependence endows random graph models with desirable properties which make them amenable to statistical inference. We show that random graph models with local dependence satisfy a natural domain consistency condition which every model should satisfy, but conventional exponential family random graph models do not satisfy. In addition, we establish a central limit theorem for random graph models with local dependence, which suggests that random graph models with local dependence are amenable to statistical inference. We discuss how random graph models with local dependence can be constructed by exploiting either observed or unobserved neighbourhood structure. In the absence of observed neighbourhood structure, we take a Bayesian view and express the uncertainty about the neighbourhood structure by specifying a prior on a set of suitable neighbourhood structures. We present simulation results and applications to two real world networks with ‘ground truth’.
dc.language.iso eng
dc.publisher Wiley
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Wiley.
dc.title Local dependence in random graph models: characterization, properties and statistical inference
dc.type Journal article
dc.citation.journalTitle Journal of the Royal Statistical Society: Series B (Statistical Methodology)
dc.subject.keywordExponential families
Local dependence
M-dependence
Model degeneracy
Social networks
Weak dependence
dc.citation.volumeNumber 77
dc.citation.issueNumber 3
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1111/rssb.12081
dc.identifier.pmcid PMC4637985
dc.identifier.pmid 26560142
dc.type.publication post-print
dc.citation.firstpage 647
dc.citation.lastpage 676


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